ZrC–NbC synergistic regulation of thermal expansion behavior and multi-scale mechanism in in-situ TiC-reinforced titanium matrix composites
Source abstract
Abstract
This work investigates in-situ TiC-reinforced Ti-5wt%ZrC-xNbC (x = 0, 3, 6, 9) composites. Using multi-scale characterization and ANSYS simulations, we reveal the synergistic mechanism of ZrC–NbC on thermal expansion behavior. ZrC provides heterogeneous nucleation sites for TiC refinement, while NbC supplies Nb to stabilize the β-phase, increasing its volume fraction from 12.3% to 41.7%. This novel 'nucleation regulation-phase stabilization' synergistic strategy overcomes the limitations of single carbide regulation and significantly optimizes material properties. With the increase of NbC content, matrix grains are gradually refined and the TiC reinforcement phase shows a dispersion-then-agglomeration trend, with 6 wt% NbC as the optimal content. This composition yields a grain size of 38.7 ± 4.5 μm, a β-phase fraction of 41.7% and uniformly dispersed TiC particles (1.2 ± 0.1 μm). Correspondingly, the average CTE of the composite (35–500 °C) is reduced to 8.2 × 10 −6 /°C, a 21.9% reduction compared with the sample without NbC addition. Mechanism analysis verifies fine-grain boundary thermal resistance and low β -phase anisotropy (Δ α = 0.7 × 10 −6 /°C) are core mechanisms, consistent with relevant theories. Excessive NbC (9 wt%) induces TiC agglomeration (5∼8 μm), causing stress concentration (S = 1.32) and CTE rebound to 9.5 × 10 −6 /°C. ANSYS simulation (error ≤5%) verifies the cross-scale mechanism, and a quantitative composition-microstructure-thermal expansion model is established, providing a novel synergistic strategy for high-temperature titanium matrix composites’ thermal stability design.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Aluminum Alloys Composites Properties · Titanium Alloys Microstructure and Properties · Advanced materials and composites
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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